Nexdata's comprehensive upgrade in supporting ai applications in healthcare

In the modern field of artificial intelligence, the success of an algorithm depends on the quality of the data. As the importance of data in artificial intelligence models becomes increasingly prominent, it becomes crucial to collect and make full use of high-quality data. This article will help you better understand the core role of data in artificial intelligence programs.

Medical and healthcare is one of the industries where artificial intelligence has made significant strides. According to statistics, the global AI in healthcare market is expected to reach $187.95 billion by 2030. Currently, AI has shown remarkable achievements in various healthcare domains, including assisting in diagnosis, drug development, data management, and clinical decision-making.

 

By leveraging AI, medical efficiency can be improved, costs can be reduced, and the overall healthcare quality can be enhanced, providing patients with better treatment outcomes and diagnostic experiences.

 

Nexdata offers high-quality ai data services and specialized annotation tools for the diagnosis and treatment sector, drug development, scientific care, and other segments. In this case study, we will share various types of medical data requirements and processing solutions to showcase how Nexdata has successfully helped clients address their challenges.

 

Case Study 1: Collection of Sports and Fitness Medical Q&A Data

 

Project Overview:

A popular domestic fitness app development team in China is working on a generative AI model based on a light medical fitness scenario. They required the collection of 1 million sets of professional sports and fitness medical Q&A data, including user inquiries and expert responses, covering health, weight loss, exercise, and medical topics.

 

Challenges:

The project required a large number of professionals with expertise in various medical disciplines to collect Q&A content while ensuring the accuracy and quality of the annotations.

 

Results:

Nexdata's project team quickly identified and selected 400 medical professionals, ensuring both data quality and efficiency. The project was successfully delivered to the client within a month.

 

Case Study 2: Annotation of Medical Literature

 

Project Overview:

A client involved in researching medications for a specific disease needed the annotation of 5,000 English medical literature documents. The annotation included filling in Q&A content after reading the literature and tagging information such as disease, genes, epidemiological indicators, sample size, and research directions.

 

Challenges:

The project involved English content presentation, and the literature had privacy constraints that required strict prevention of ai data collection.

 

Results:

Nexdata rapidly assembled a professional annotation team that met the client's requirements. To ensure data security and prevent leaks, the project was processed in Nexdata's secure annotation facility.

 

Case Study 3: Annotation of Microbial and Tumor Cell Image

 

Project Overview:

A globally renowned medical testing instrument development company needed to upgrade its detection equipment for early tumor screening. The project required the annotation of nine types of cells, totaling 70,000 images. The client aimed to complete data delivery within two weeks.

 

Challenges:

The project faced time constraints and required efficient annotation for various cell types, including alignment of lactobacillus and miscellaneous bacteria quantities. Some hyphae were covered by other cells, necessitating annotation.

 

Results:

Nexdata's AI R&D team developed a cell pre-recognition capability for semi-automatic annotation. By combining intelligent pre-annotation and cross-quality checks by professional personnel, the data collection and annotation was delivered ahead of schedule at a low cost and passed the full acceptance.

 

Case Study 4: Annotation of Medical Scenario Voice

 

Overview:

With the widespread use of electronic medical records, doctors spend a significant amount of time on record-keeping. A globally large-scale company in the medical field engaged in speech recognition software sought to present accurate intelligent diagnostic voice communication for patients. They required correction annotation for medical voice data.

 

Challenges:

The provided instances involved patient-expert voice conversations, requiring transcription of consultation dialogues. However, medical consultations often include numerous specialized terms, and existing annotation tools for medical voice pre-recognition were suboptimal.

 

Results:

Nexdata's self-developed voice pre-recognition achieved efficient and high accuracy automated transcription, significantly reducing costs. Medical domain professionals then performed rapid correction annotation, completing the correction process.

 

Case Study 5: Private Deployment of Medical Image Annotation Tools

 

Overview:

Many medical enterprises have stricter regulatory measures for data management and security compared to other industries. A client wished to deploy data annotation services and management tools on their platform, meeting their requirements for multi-type, multi-process, and multi-role management.

 

Challenges:

AI application in the medical industry is not yet widely prevalent, and mature data processing tools for medical data are scarce. Clients were concerned about data security, privacy, and the reliability and stability of the system.

 

Results:

After thoroughly understanding the client's needs, Nexdata recommended its self-developed data annotation platform, which can fulfill multi-type ai data annotation services requirements for 2D & 3D medical images, electrophysiology, optical scanning, voice, video, text, and more.

 

In Conclusion:

As global healthcare enterprises and research institutions leverage advanced AI technology to empower their industries and address core challenges, further accelerating research progress, we believe that, through multidimensional collaboration, AI in healthcare can quickly achieve fusion and innovation in new trends and scenarios. The future will witness more new technologies, products, and applications.

High-quality datasets are the foundation for the success of artificial intelligence. Therefore, all industries need to continue investing in data infrastructure to make sure the accuracy and diversity of data collection. From smart city to precision medicare, from education equality to environment protection, the future potential of AI will binding with data system to provide dynamic for society and economy.